State-of-the-art speech recognition systems have seen tremendous progress in the last few years, with end-to-end architectures becoming a default modeling choice. While end-to-end models yield impressive WER reductions and work well for certain user populations [1]}, [2]}, they severely underperform when confronted with out-of-domain test utterances in target accents that are unseen or rarely seen during training. A common solution to address such mismatched settings is to adapt a well-trained, speaker-independent ASR model with a small amount of target-specific data to build personalized models for each target setting.
